Análisis de modelos SOM - Frecuencia datos de entrada: mes

Modelo

• ID: 324
• Descripción:
• Frecuencia: mes
• Variables: fecha_cnt, tmax, tmin, precip, nevada, prof_nieve
• Dimensiones del mapa: 10,10
• Iteraciones: 10000
• Parámetros adicionales:

source("../../lib/som-utils.R")

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

source("../../lib/maps-utils.R")

Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

Carga del modelo desde disco

mpr.set_base_path_analysis()
model <- mpr.load_model("som-324.rds.xz")
summary(model)

SOM of size 10x10 with a hexagonal topology and a bubble neighbourhood function.
The number of data layers is 1.
Distance measure(s) used: sumofsquares.
Training data included: 94881 objects.
Mean distance to the closest unit in the map: 0.312.

plot(model, type="changes")

Carga del dataset de entrada

df <- mpr.load_data("datos_mes.csv.xz")

df

summary(df)

 id_estacion           fecha             fecha_cnt           tmax      
 Length:94881       Length:94881       Min.   : 1.000   Min.   :-53.0  
 Class :character   Class :character   1st Qu.: 4.000   1st Qu.:148.0  
 Mode  :character   Mode  :character   Median : 6.000   Median :198.0  
                                       Mean   : 6.497   Mean   :200.2  
                                       3rd Qu.: 9.000   3rd Qu.:255.0  
                                       Max.   :12.000   Max.   :403.0  
      tmin             precip           nevada           prof_nieve      
 Min.   :-121.00   Min.   :  0.00   Min.   :0.000000   Min.   :   0.000  
 1st Qu.:  53.00   1st Qu.:  3.00   1st Qu.:0.000000   1st Qu.:   0.000  
 Median :  98.00   Median : 10.00   Median :0.000000   Median :   0.000  
 Mean   :  98.86   Mean   : 16.25   Mean   :0.000295   Mean   :   0.467  
 3rd Qu.: 148.00   3rd Qu.: 22.00   3rd Qu.:0.000000   3rd Qu.:   0.000  
 Max.   : 254.00   Max.   :422.00   Max.   :6.000000   Max.   :1834.000  
    longitud        latitud            altitud      
 Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:38.28   1st Qu.: -5.6417   1st Qu.:  42.0  
 Median :40.82   Median : -3.4500   Median : 247.0  
 Mean   :39.66   Mean   : -3.4350   Mean   : 418.5  
 3rd Qu.:42.08   3rd Qu.:  0.4914   3rd Qu.: 656.0  
 Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

Carga de los mapas

world <- ne_countries(scale = "medium", returnclass = "sf")
spain <- subset(world, admin == "Spain")

Mapa de densidad

plot(model, type="count", shape = "straight", palette.name = mpr.degrade.bleu)

Número de elementos en cada celda:

nb <- table(model$unit.classif)
print(nb)

   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
 161  219 1112 1084 1060 1377 1008  243   19    2  410  711  581   10  962 1325 
  17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32 
 953  670   38    5  450 1038 1635  325 1309 1205 1348   75  272  119 1338 1393 
  33   34   35   36   37   38   39   40   41   42   43   44   45   46   47   48 
1421 1435 1333 1382 1626 1216 1112  460  861 1374 1218 1424  763 1024  225 1360 
  49   50   51   52   53   54   55   56   57   58   59   60   61   62   63   64 
 924  456 1245 1097 1298 1450 1240 1009 1161  988 1185  944 1018 1418 1373 1632 
  65   66   67   68   69   70   71   72   73   74   75   76   77   78   79   80 
1591 1204 1479 1184  675  401  856 1014 1438 1880 1839  859 1279  998  845  270 
  81   82   83   84   85   86   87   88   89   90   91   92   93   94   95   96 
1531 1631 1575 1025 1258  479  593  600  382   94 1733 1892 1668 1099  707  189 
  97   98   99  100 
1146  227   84   27 

Comprobación de nodos vacíos:

dim_model <- 10*10;
len_nb = length(nb);
empty_nodes <- dim_model != len_nb;
if (empty_nodes) {
  print(paste("[Warning] Existen nodos vacíos: ", len_nb, "/", dim_model))
}

Mapa de distancia entre vecinos

plot(model, type="dist.neighbours", shape = "straight")

Influencia de las variables

model_colnames = c("fecha_cnt", "tmax", "tmin", "precip", "nevada", "prof_nieve")
model_ncol = length(model_colnames)

Mapa de variables.

plot(model, shape = "straight")

Mapa de calor por variable

par(mfrow=c(3,4))
for (j in 1:model_ncol) {
  plot(model, type="property", property=getCodes(model,1)[,j],
    palette.name=mpr.coolBlueHotRed,
    main=model_colnames[j],
    cex=0.5, shape = "straight")
}

Correlación para cada columna del vector de nodos

if (!empty_nodes) {
  cor <- apply(getCodes(model,1), 2, mpr.weighted.correlation, w=nb, som=model)
  print(cor)
}

      fecha_cnt       tmax       tmin      precip       nevada  prof_nieve
[1,] -0.7603017 -0.4321094 -0.4469328  0.32007045  0.005222191  0.04860608
[2,]  0.1619183  0.8127935  0.8536625 -0.02583183 -0.015973666 -0.04665903

Representación de cada variable en un mapa de factores:

if (!empty_nodes) {
  par(mfrow=c(1,1))
  plot(cor[1,], cor[2,], xlim=c(-1,1), ylim=c(-1,1), type="n")
  lines(c(-1,1),c(0,0))
  lines(c(0,0),c(-1,1))
  text(cor[1,], cor[2,], labels=model_colnames, cex=0.75)
  symbols(0,0,circles=1,inches=F,add=T)
}

Importancia de cada variable - varianza ponderada por el tamaño de la celda:

if (!empty_nodes) {
  sigma2 <- sqrt(apply(getCodes(model,1),2,function(x,effectif)
     {m<-sum(effectif*(x-weighted.mean(x,effectif))^2)/(sum(effectif)-1)},
     effectif=nb))
  print(sort(sigma2,decreasing=T))
}

prof_nieve  fecha_cnt       tmax     precip       tmin     nevada 
 0.9831934  0.9778331  0.9757191  0.9745181  0.9744643  0.9600090 

Clustering

if (!empty_nodes) {
  hac <- mpr.hac(model, nb)
}

Visualización de 3 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=3)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=3)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax            tmin             precip      
 Min.   : 1.000   Min.   :-53.0   Min.   :-121.00   Min.   :  0.00  
 1st Qu.: 4.000   1st Qu.:148.0   1st Qu.:  53.00   1st Qu.:  3.00  
 Median : 6.000   Median :198.0   Median :  98.00   Median : 10.00  
 Mean   : 6.497   Mean   :200.2   Mean   :  98.87   Mean   : 16.25  
 3rd Qu.: 9.000   3rd Qu.:255.0   3rd Qu.: 148.00   3rd Qu.: 22.00  
 Max.   :12.000   Max.   :403.0   Max.   : 254.00   Max.   :422.00  
     nevada            prof_nieve          longitud        latitud        
 Min.   :0.0000000   Min.   :  0.0000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0.0000000   1st Qu.:  0.0000   1st Qu.:38.28   1st Qu.: -5.6417  
 Median :0.0000000   Median :  0.0000   Median :40.82   Median : -3.4500  
 Mean   :0.0001897   Mean   :  0.3974   Mean   :39.66   Mean   : -3.4350  
 3rd Qu.:0.0000000   3rd Qu.:  0.0000   3rd Qu.:42.08   3rd Qu.:  0.4914  
 Max.   :3.0000000   Max.   :892.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  42.0  
 Median : 247.0  
 Mean   : 418.5  
 3rd Qu.: 656.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt      tmax            tmin        precip          nevada   
 Min.   :2   Min.   :71.00   Min.   :-1   Min.   :14.00   Min.   :4.0  
 1st Qu.:2   1st Qu.:72.25   1st Qu.:-1   1st Qu.:15.25   1st Qu.:4.5  
 Median :2   Median :73.50   Median :-1   Median :16.50   Median :5.0  
 Mean   :2   Mean   :73.50   Mean   :-1   Mean   :16.50   Mean   :5.0  
 3rd Qu.:2   3rd Qu.:74.75   3rd Qu.:-1   3rd Qu.:17.75   3rd Qu.:5.5  
 Max.   :2   Max.   :76.00   Max.   :-1   Max.   :19.00   Max.   :6.0  
   prof_nieve    longitud        latitud          altitud     
 Min.   : 5   Min.   :40.48   Min.   :-3.450   Min.   :263.0  
 1st Qu.: 7   1st Qu.:40.78   1st Qu.:-2.846   1st Qu.:349.3  
 Median : 9   Median :41.08   Median :-2.242   Median :435.6  
 Mean   : 9   Mean   :41.08   Mean   :-2.242   Mean   :435.6  
 3rd Qu.:11   3rd Qu.:41.37   3rd Qu.:-1.637   3rd Qu.:521.8  
 Max.   :13   Max.   :41.67   Max.   :-1.033   Max.   :608.1  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt        tmax           tmin           precip         nevada 
 Min.   :2.0   Min.   :-4.0   Min.   :-51.0   Min.   :19.0   Min.   :0  
 1st Qu.:2.0   1st Qu.: 0.0   1st Qu.:-48.0   1st Qu.:33.0   1st Qu.:0  
 Median :3.0   Median :35.0   Median :-26.0   Median :49.0   Median :0  
 Mean   :2.6   Mean   :20.6   Mean   :-32.8   Mean   :41.4   Mean   :0  
 3rd Qu.:3.0   3rd Qu.:36.0   3rd Qu.:-23.0   3rd Qu.:50.0   3rd Qu.:0  
 Max.   :3.0   Max.   :36.0   Max.   :-16.0   Max.   :56.0   Max.   :0  
   prof_nieve      longitud        latitud         altitud    
 Min.   :1017   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:1073   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :1168   Median :40.78   Median :-4.01   Median :1894  
 Mean   :1317   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:1494   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :1834   Max.   :40.78   Max.   :-4.01   Max.   :1894  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

Visualización de 4 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=4)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=4)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax            tmin             precip      
 Min.   : 1.000   Min.   :-53.0   Min.   :-121.00   Min.   :  0.00  
 1st Qu.: 4.000   1st Qu.:148.0   1st Qu.:  53.00   1st Qu.:  3.00  
 Median : 6.000   Median :198.0   Median :  98.00   Median : 10.00  
 Mean   : 6.497   Mean   :200.2   Mean   :  98.88   Mean   : 16.25  
 3rd Qu.: 9.000   3rd Qu.:255.0   3rd Qu.: 148.00   3rd Qu.: 22.00  
 Max.   :12.000   Max.   :403.0   Max.   : 254.00   Max.   :422.00  
     nevada    prof_nieve          longitud        latitud        
 Min.   :0   Min.   :  0.0000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0   1st Qu.:  0.0000   1st Qu.:38.28   1st Qu.: -5.6417  
 Median :0   Median :  0.0000   Median :40.82   Median : -3.4501  
 Mean   :0   Mean   :  0.3973   Mean   :39.66   Mean   : -3.4351  
 3rd Qu.:0   3rd Qu.:  0.0000   3rd Qu.:42.08   3rd Qu.:  0.4914  
 Max.   :0   Max.   :892.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  42.0  
 Median : 247.0  
 Mean   : 418.5  
 3rd Qu.: 656.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt      tmax            tmin        precip          nevada   
 Min.   :2   Min.   :71.00   Min.   :-1   Min.   :14.00   Min.   :4.0  
 1st Qu.:2   1st Qu.:72.25   1st Qu.:-1   1st Qu.:15.25   1st Qu.:4.5  
 Median :2   Median :73.50   Median :-1   Median :16.50   Median :5.0  
 Mean   :2   Mean   :73.50   Mean   :-1   Mean   :16.50   Mean   :5.0  
 3rd Qu.:2   3rd Qu.:74.75   3rd Qu.:-1   3rd Qu.:17.75   3rd Qu.:5.5  
 Max.   :2   Max.   :76.00   Max.   :-1   Max.   :19.00   Max.   :6.0  
   prof_nieve    longitud        latitud          altitud     
 Min.   : 5   Min.   :40.48   Min.   :-3.450   Min.   :263.0  
 1st Qu.: 7   1st Qu.:40.78   1st Qu.:-2.846   1st Qu.:349.3  
 Median : 9   Median :41.08   Median :-2.242   Median :435.6  
 Mean   : 9   Mean   :41.08   Mean   :-2.242   Mean   :435.6  
 3rd Qu.:11   3rd Qu.:41.37   3rd Qu.:-1.637   3rd Qu.:521.8  
 Max.   :13   Max.   :41.67   Max.   :-1.033   Max.   :608.1  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt         tmax             tmin            precip     
 Min.   : 1.0   Min.   : 69.00   Min.   :-24.00   Min.   : 3.00  
 1st Qu.: 2.0   1st Qu.: 86.25   1st Qu.:-12.50   1st Qu.: 6.00  
 Median : 7.0   Median : 92.00   Median :  6.50   Median :10.50  
 Mean   : 6.8   Mean   : 96.80   Mean   :  7.90   Mean   :11.40  
 3rd Qu.:12.0   3rd Qu.: 96.75   3rd Qu.: 29.25   3rd Qu.:14.75  
 Max.   :12.0   Max.   :140.00   Max.   : 35.00   Max.   :24.00  
     nevada       prof_nieve     longitud        latitud          altitud     
 Min.   :1.00   Min.   :0.0   Min.   :40.48   Min.   :-3.450   Min.   :263.0  
 1st Qu.:1.25   1st Qu.:0.0   1st Qu.:40.48   1st Qu.:-3.450   1st Qu.:263.0  
 Median :2.00   Median :0.5   Median :41.08   Median :-2.242   Median :435.6  
 Mean   :1.80   Mean   :1.1   Mean   :41.08   Mean   :-2.242   Mean   :435.6  
 3rd Qu.:2.00   3rd Qu.:1.0   3rd Qu.:41.67   3rd Qu.:-1.033   3rd Qu.:608.1  
 Max.   :3.00   Max.   :7.0   Max.   :41.67   Max.   :-1.033   Max.   :608.1  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt        tmax           tmin           precip         nevada 
 Min.   :2.0   Min.   :-4.0   Min.   :-51.0   Min.   :19.0   Min.   :0  
 1st Qu.:2.0   1st Qu.: 0.0   1st Qu.:-48.0   1st Qu.:33.0   1st Qu.:0  
 Median :3.0   Median :35.0   Median :-26.0   Median :49.0   Median :0  
 Mean   :2.6   Mean   :20.6   Mean   :-32.8   Mean   :41.4   Mean   :0  
 3rd Qu.:3.0   3rd Qu.:36.0   3rd Qu.:-23.0   3rd Qu.:50.0   3rd Qu.:0  
 Max.   :3.0   Max.   :36.0   Max.   :-16.0   Max.   :56.0   Max.   :0  
   prof_nieve      longitud        latitud         altitud    
 Min.   :1017   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:1073   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :1168   Median :40.78   Median :-4.01   Median :1894  
 Mean   :1317   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:1494   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :1834   Max.   :40.78   Max.   :-4.01   Max.   :1894  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

Visualización de 5 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=5)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=5)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax            tmin            precip           nevada 
 Min.   : 1.000   Min.   :-53.0   Min.   :-121.0   Min.   :  0.00   Min.   :0  
 1st Qu.: 4.000   1st Qu.:149.0   1st Qu.:  53.0   1st Qu.:  3.00   1st Qu.:0  
 Median : 6.000   Median :198.0   Median :  98.0   Median : 10.00   Median :0  
 Mean   : 6.498   Mean   :200.3   Mean   :  98.9   Mean   : 16.24   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:255.0   3rd Qu.: 148.0   3rd Qu.: 22.00   3rd Qu.:0  
 Max.   :12.000   Max.   :403.0   Max.   : 254.0   Max.   :422.00   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   :  0.0000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:  0.0000   1st Qu.:38.28   1st Qu.: -5.6417   1st Qu.:  42.0  
 Median :  0.0000   Median :40.82   Median : -3.4500   Median : 247.0  
 Mean   :  0.2746   Mean   :39.66   Mean   : -3.4350   Mean   : 418.2  
 3rd Qu.:  0.0000   3rd Qu.:42.08   3rd Qu.:  0.4914   3rd Qu.: 656.0  
 Max.   :429.0000   Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax             tmin            precip           nevada 
 Min.   :1.000   Min.   :-12.00   Min.   :-72.00   Min.   : 19.00   Min.   :0  
 1st Qu.:2.000   1st Qu.:  9.50   1st Qu.:-43.50   1st Qu.: 41.00   1st Qu.:0  
 Median :2.000   Median : 15.00   Median :-34.00   Median : 56.00   Median :0  
 Mean   :2.368   Mean   : 23.21   Mean   :-31.95   Mean   : 65.79   Mean   :0  
 3rd Qu.:3.000   3rd Qu.: 28.50   3rd Qu.:-25.00   3rd Qu.: 82.00   3rd Qu.:0  
 Max.   :4.000   Max.   : 76.00   Max.   :  8.00   Max.   :180.00   Max.   :0  
   prof_nieve       longitud        latitud         altitud    
 Min.   :465.0   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:531.0   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :606.0   Median :40.78   Median :-4.01   Median :1894  
 Mean   :612.8   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:661.5   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :892.0   Max.   :40.78   Max.   :-4.01   Max.   :1894  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt      tmax            tmin        precip          nevada   
 Min.   :2   Min.   :71.00   Min.   :-1   Min.   :14.00   Min.   :4.0  
 1st Qu.:2   1st Qu.:72.25   1st Qu.:-1   1st Qu.:15.25   1st Qu.:4.5  
 Median :2   Median :73.50   Median :-1   Median :16.50   Median :5.0  
 Mean   :2   Mean   :73.50   Mean   :-1   Mean   :16.50   Mean   :5.0  
 3rd Qu.:2   3rd Qu.:74.75   3rd Qu.:-1   3rd Qu.:17.75   3rd Qu.:5.5  
 Max.   :2   Max.   :76.00   Max.   :-1   Max.   :19.00   Max.   :6.0  
   prof_nieve    longitud        latitud          altitud     
 Min.   : 5   Min.   :40.48   Min.   :-3.450   Min.   :263.0  
 1st Qu.: 7   1st Qu.:40.78   1st Qu.:-2.846   1st Qu.:349.3  
 Median : 9   Median :41.08   Median :-2.242   Median :435.6  
 Mean   : 9   Mean   :41.08   Mean   :-2.242   Mean   :435.6  
 3rd Qu.:11   3rd Qu.:41.37   3rd Qu.:-1.637   3rd Qu.:521.8  
 Max.   :13   Max.   :41.67   Max.   :-1.033   Max.   :608.1  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt         tmax             tmin            precip     
 Min.   : 1.0   Min.   : 69.00   Min.   :-24.00   Min.   : 3.00  
 1st Qu.: 2.0   1st Qu.: 86.25   1st Qu.:-12.50   1st Qu.: 6.00  
 Median : 7.0   Median : 92.00   Median :  6.50   Median :10.50  
 Mean   : 6.8   Mean   : 96.80   Mean   :  7.90   Mean   :11.40  
 3rd Qu.:12.0   3rd Qu.: 96.75   3rd Qu.: 29.25   3rd Qu.:14.75  
 Max.   :12.0   Max.   :140.00   Max.   : 35.00   Max.   :24.00  
     nevada       prof_nieve     longitud        latitud          altitud     
 Min.   :1.00   Min.   :0.0   Min.   :40.48   Min.   :-3.450   Min.   :263.0  
 1st Qu.:1.25   1st Qu.:0.0   1st Qu.:40.48   1st Qu.:-3.450   1st Qu.:263.0  
 Median :2.00   Median :0.5   Median :41.08   Median :-2.242   Median :435.6  
 Mean   :1.80   Mean   :1.1   Mean   :41.08   Mean   :-2.242   Mean   :435.6  
 3rd Qu.:2.00   3rd Qu.:1.0   3rd Qu.:41.67   3rd Qu.:-1.033   3rd Qu.:608.1  
 Max.   :3.00   Max.   :7.0   Max.   :41.67   Max.   :-1.033   Max.   :608.1  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt        tmax           tmin           precip         nevada 
 Min.   :2.0   Min.   :-4.0   Min.   :-51.0   Min.   :19.0   Min.   :0  
 1st Qu.:2.0   1st Qu.: 0.0   1st Qu.:-48.0   1st Qu.:33.0   1st Qu.:0  
 Median :3.0   Median :35.0   Median :-26.0   Median :49.0   Median :0  
 Mean   :2.6   Mean   :20.6   Mean   :-32.8   Mean   :41.4   Mean   :0  
 3rd Qu.:3.0   3rd Qu.:36.0   3rd Qu.:-23.0   3rd Qu.:50.0   3rd Qu.:0  
 Max.   :3.0   Max.   :36.0   Max.   :-16.0   Max.   :56.0   Max.   :0  
   prof_nieve      longitud        latitud         altitud    
 Min.   :1017   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:1073   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :1168   Median :40.78   Median :-4.01   Median :1894  
 Mean   :1317   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:1494   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :1834   Max.   :40.78   Max.   :-4.01   Max.   :1894  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

Visualización de 6 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=6)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=6)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax            tmin             precip      
 Min.   : 1.000   Min.   :-53.0   Min.   :-121.00   Min.   :  0.00  
 1st Qu.: 4.000   1st Qu.:149.0   1st Qu.:  53.00   1st Qu.:  3.00  
 Median : 6.000   Median :198.0   Median :  98.00   Median : 10.00  
 Mean   : 6.499   Mean   :200.3   Mean   :  98.95   Mean   : 16.23  
 3rd Qu.: 9.000   3rd Qu.:255.0   3rd Qu.: 148.00   3rd Qu.: 22.00  
 Max.   :12.000   Max.   :403.0   Max.   : 254.00   Max.   :422.00  
     nevada    prof_nieve          longitud        latitud        
 Min.   :0   Min.   :  0.0000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0   1st Qu.:  0.0000   1st Qu.:38.28   1st Qu.: -5.6417  
 Median :0   Median :  0.0000   Median :40.82   Median : -3.4500  
 Mean   :0   Mean   :  0.1623   Mean   :39.66   Mean   : -3.4348  
 3rd Qu.:0   3rd Qu.:  0.0000   3rd Qu.:42.08   3rd Qu.:  0.4914  
 Max.   :0   Max.   :197.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  42.0  
 Median : 247.0  
 Mean   : 417.6  
 3rd Qu.: 656.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax             tmin            precip           nevada 
 Min.   :1.000   Min.   :-12.00   Min.   :-72.00   Min.   : 19.00   Min.   :0  
 1st Qu.:2.000   1st Qu.:  9.50   1st Qu.:-43.50   1st Qu.: 41.00   1st Qu.:0  
 Median :2.000   Median : 15.00   Median :-34.00   Median : 56.00   Median :0  
 Mean   :2.368   Mean   : 23.21   Mean   :-31.95   Mean   : 65.79   Mean   :0  
 3rd Qu.:3.000   3rd Qu.: 28.50   3rd Qu.:-25.00   3rd Qu.: 82.00   3rd Qu.:0  
 Max.   :4.000   Max.   : 76.00   Max.   :  8.00   Max.   :180.00   Max.   :0  
   prof_nieve       longitud        latitud         altitud    
 Min.   :465.0   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:531.0   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :606.0   Median :40.78   Median :-4.01   Median :1894  
 Mean   :612.8   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:661.5   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :892.0   Max.   :40.78   Max.   :-4.01   Max.   :1894  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt      tmax            tmin        precip          nevada   
 Min.   :2   Min.   :71.00   Min.   :-1   Min.   :14.00   Min.   :4.0  
 1st Qu.:2   1st Qu.:72.25   1st Qu.:-1   1st Qu.:15.25   1st Qu.:4.5  
 Median :2   Median :73.50   Median :-1   Median :16.50   Median :5.0  
 Mean   :2   Mean   :73.50   Mean   :-1   Mean   :16.50   Mean   :5.0  
 3rd Qu.:2   3rd Qu.:74.75   3rd Qu.:-1   3rd Qu.:17.75   3rd Qu.:5.5  
 Max.   :2   Max.   :76.00   Max.   :-1   Max.   :19.00   Max.   :6.0  
   prof_nieve    longitud        latitud          altitud     
 Min.   : 5   Min.   :40.48   Min.   :-3.450   Min.   :263.0  
 1st Qu.: 7   1st Qu.:40.78   1st Qu.:-2.846   1st Qu.:349.3  
 Median : 9   Median :41.08   Median :-2.242   Median :435.6  
 Mean   : 9   Mean   :41.08   Mean   :-2.242   Mean   :435.6  
 3rd Qu.:11   3rd Qu.:41.37   3rd Qu.:-1.637   3rd Qu.:521.8  
 Max.   :13   Max.   :41.67   Max.   :-1.033   Max.   :608.1  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt         tmax             tmin            precip     
 Min.   : 1.0   Min.   : 69.00   Min.   :-24.00   Min.   : 3.00  
 1st Qu.: 2.0   1st Qu.: 86.25   1st Qu.:-12.50   1st Qu.: 6.00  
 Median : 7.0   Median : 92.00   Median :  6.50   Median :10.50  
 Mean   : 6.8   Mean   : 96.80   Mean   :  7.90   Mean   :11.40  
 3rd Qu.:12.0   3rd Qu.: 96.75   3rd Qu.: 29.25   3rd Qu.:14.75  
 Max.   :12.0   Max.   :140.00   Max.   : 35.00   Max.   :24.00  
     nevada       prof_nieve     longitud        latitud          altitud     
 Min.   :1.00   Min.   :0.0   Min.   :40.48   Min.   :-3.450   Min.   :263.0  
 1st Qu.:1.25   1st Qu.:0.0   1st Qu.:40.48   1st Qu.:-3.450   1st Qu.:263.0  
 Median :2.00   Median :0.5   Median :41.08   Median :-2.242   Median :435.6  
 Mean   :1.80   Mean   :1.1   Mean   :41.08   Mean   :-2.242   Mean   :435.6  
 3rd Qu.:2.00   3rd Qu.:1.0   3rd Qu.:41.67   3rd Qu.:-1.033   3rd Qu.:608.1  
 Max.   :3.00   Max.   :7.0   Max.   :41.67   Max.   :-1.033   Max.   :608.1  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt           tmax             tmin            precip          nevada 
 Min.   : 1.000   Min.   :-12.00   Min.   :-63.00   Min.   : 5.00   Min.   :0  
 1st Qu.: 1.250   1st Qu.: 13.50   1st Qu.:-38.00   1st Qu.:25.25   1st Qu.:0  
 Median : 2.000   Median : 27.50   Median :-26.00   Median :47.00   Median :0  
 Mean   : 3.342   Mean   : 31.89   Mean   :-25.89   Mean   :46.53   Mean   :0  
 3rd Qu.: 3.750   3rd Qu.: 47.75   3rd Qu.:-15.00   3rd Qu.:66.75   3rd Qu.:0  
 Max.   :12.000   Max.   : 96.00   Max.   : 28.00   Max.   :92.00   Max.   :0  
   prof_nieve       longitud        latitud         altitud    
 Min.   :199.0   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:229.5   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :270.0   Median :40.78   Median :-4.01   Median :1894  
 Mean   :280.6   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:318.0   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :429.0   Max.   :40.78   Max.   :-4.01   Max.   :1894  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt        tmax           tmin           precip         nevada 
 Min.   :2.0   Min.   :-4.0   Min.   :-51.0   Min.   :19.0   Min.   :0  
 1st Qu.:2.0   1st Qu.: 0.0   1st Qu.:-48.0   1st Qu.:33.0   1st Qu.:0  
 Median :3.0   Median :35.0   Median :-26.0   Median :49.0   Median :0  
 Mean   :2.6   Mean   :20.6   Mean   :-32.8   Mean   :41.4   Mean   :0  
 3rd Qu.:3.0   3rd Qu.:36.0   3rd Qu.:-23.0   3rd Qu.:50.0   3rd Qu.:0  
 Max.   :3.0   Max.   :36.0   Max.   :-16.0   Max.   :56.0   Max.   :0  
   prof_nieve      longitud        latitud         altitud    
 Min.   :1017   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:1073   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :1168   Median :40.78   Median :-4.01   Median :1894  
 Mean   :1317   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:1494   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :1834   Max.   :40.78   Max.   :-4.01   Max.   :1894  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

Visualización de 8 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=8)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=8)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)
  df.cluster07 <- subset(df, cluster==7)
  df.cluster08 <- subset(df, cluster==8)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt          tmax            tmin             precip           nevada 
 Min.   : 1.00   Min.   :-53.0   Min.   :-121.00   Min.   :  0.00   Min.   :0  
 1st Qu.: 2.00   1st Qu.:119.0   1st Qu.:  26.00   1st Qu.:  6.00   1st Qu.:0  
 Median : 4.00   Median :152.0   Median :  56.00   Median : 15.00   Median :0  
 Mean   : 6.04   Mean   :147.2   Mean   :  52.32   Mean   : 20.74   Mean   :0  
 3rd Qu.:11.00   3rd Qu.:178.0   3rd Qu.:  80.00   3rd Qu.: 29.00   3rd Qu.:0  
 Max.   :12.00   Max.   :326.0   Max.   : 211.00   Max.   :155.00   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   :  0.0000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.:  0.0000   1st Qu.:39.49   1st Qu.: -5.3456   1st Qu.:  69.0  
 Median :  0.0000   Median :41.15   Median : -2.7331   Median : 412.0  
 Mean   :  0.2979   Mean   :40.51   Mean   : -2.8171   Mean   : 529.6  
 3rd Qu.:  0.0000   3rd Qu.:42.36   3rd Qu.:  0.4942   3rd Qu.: 775.0  
 Max.   :197.0000   Max.   :43.57   Max.   :  4.2156   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt          tmax             tmin            precip           nevada 
 Min.   :1.000   Min.   :-12.00   Min.   :-72.00   Min.   : 19.00   Min.   :0  
 1st Qu.:2.000   1st Qu.:  9.50   1st Qu.:-43.50   1st Qu.: 41.00   1st Qu.:0  
 Median :2.000   Median : 15.00   Median :-34.00   Median : 56.00   Median :0  
 Mean   :2.368   Mean   : 23.21   Mean   :-31.95   Mean   : 65.79   Mean   :0  
 3rd Qu.:3.000   3rd Qu.: 28.50   3rd Qu.:-25.00   3rd Qu.: 82.00   3rd Qu.:0  
 Max.   :4.000   Max.   : 76.00   Max.   :  8.00   Max.   :180.00   Max.   :0  
   prof_nieve       longitud        latitud         altitud    
 Min.   :465.0   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:531.0   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :606.0   Median :40.78   Median :-4.01   Median :1894  
 Mean   :612.8   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:661.5   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :892.0   Max.   :40.78   Max.   :-4.01   Max.   :1894  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt      tmax            tmin        precip          nevada   
 Min.   :2   Min.   :71.00   Min.   :-1   Min.   :14.00   Min.   :4.0  
 1st Qu.:2   1st Qu.:72.25   1st Qu.:-1   1st Qu.:15.25   1st Qu.:4.5  
 Median :2   Median :73.50   Median :-1   Median :16.50   Median :5.0  
 Mean   :2   Mean   :73.50   Mean   :-1   Mean   :16.50   Mean   :5.0  
 3rd Qu.:2   3rd Qu.:74.75   3rd Qu.:-1   3rd Qu.:17.75   3rd Qu.:5.5  
 Max.   :2   Max.   :76.00   Max.   :-1   Max.   :19.00   Max.   :6.0  
   prof_nieve    longitud        latitud          altitud     
 Min.   : 5   Min.   :40.48   Min.   :-3.450   Min.   :263.0  
 1st Qu.: 7   1st Qu.:40.78   1st Qu.:-2.846   1st Qu.:349.3  
 Median : 9   Median :41.08   Median :-2.242   Median :435.6  
 Mean   : 9   Mean   :41.08   Mean   :-2.242   Mean   :435.6  
 3rd Qu.:11   3rd Qu.:41.37   3rd Qu.:-1.637   3rd Qu.:521.8  
 Max.   :13   Max.   :41.67   Max.   :-1.033   Max.   :608.1  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt         tmax             tmin            precip     
 Min.   : 1.0   Min.   : 69.00   Min.   :-24.00   Min.   : 3.00  
 1st Qu.: 2.0   1st Qu.: 86.25   1st Qu.:-12.50   1st Qu.: 6.00  
 Median : 7.0   Median : 92.00   Median :  6.50   Median :10.50  
 Mean   : 6.8   Mean   : 96.80   Mean   :  7.90   Mean   :11.40  
 3rd Qu.:12.0   3rd Qu.: 96.75   3rd Qu.: 29.25   3rd Qu.:14.75  
 Max.   :12.0   Max.   :140.00   Max.   : 35.00   Max.   :24.00  
     nevada       prof_nieve     longitud        latitud          altitud     
 Min.   :1.00   Min.   :0.0   Min.   :40.48   Min.   :-3.450   Min.   :263.0  
 1st Qu.:1.25   1st Qu.:0.0   1st Qu.:40.48   1st Qu.:-3.450   1st Qu.:263.0  
 Median :2.00   Median :0.5   Median :41.08   Median :-2.242   Median :435.6  
 Mean   :1.80   Mean   :1.1   Mean   :41.08   Mean   :-2.242   Mean   :435.6  
 3rd Qu.:2.00   3rd Qu.:1.0   3rd Qu.:41.67   3rd Qu.:-1.033   3rd Qu.:608.1  
 Max.   :3.00   Max.   :7.0   Max.   :41.67   Max.   :-1.033   Max.   :608.1  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt           tmax             tmin            precip          nevada 
 Min.   : 1.000   Min.   :-12.00   Min.   :-63.00   Min.   : 5.00   Min.   :0  
 1st Qu.: 1.250   1st Qu.: 13.50   1st Qu.:-38.00   1st Qu.:25.25   1st Qu.:0  
 Median : 2.000   Median : 27.50   Median :-26.00   Median :47.00   Median :0  
 Mean   : 3.342   Mean   : 31.89   Mean   :-25.89   Mean   :46.53   Mean   :0  
 3rd Qu.: 3.750   3rd Qu.: 47.75   3rd Qu.:-15.00   3rd Qu.:66.75   3rd Qu.:0  
 Max.   :12.000   Max.   : 96.00   Max.   : 28.00   Max.   :92.00   Max.   :0  
   prof_nieve       longitud        latitud         altitud    
 Min.   :199.0   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:229.5   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :270.0   Median :40.78   Median :-4.01   Median :1894  
 Mean   :280.6   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:318.0   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :429.0   Max.   :40.78   Max.   :-4.01   Max.   :1894  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt        tmax           tmin           precip         nevada 
 Min.   :2.0   Min.   :-4.0   Min.   :-51.0   Min.   :19.0   Min.   :0  
 1st Qu.:2.0   1st Qu.: 0.0   1st Qu.:-48.0   1st Qu.:33.0   1st Qu.:0  
 Median :3.0   Median :35.0   Median :-26.0   Median :49.0   Median :0  
 Mean   :2.6   Mean   :20.6   Mean   :-32.8   Mean   :41.4   Mean   :0  
 3rd Qu.:3.0   3rd Qu.:36.0   3rd Qu.:-23.0   3rd Qu.:50.0   3rd Qu.:0  
 Max.   :3.0   Max.   :36.0   Max.   :-16.0   Max.   :56.0   Max.   :0  
   prof_nieve      longitud        latitud         altitud    
 Min.   :1017   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:1073   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :1168   Median :40.78   Median :-4.01   Median :1894  
 Mean   :1317   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:1494   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :1834   Max.   :40.78   Max.   :-4.01   Max.   :1894  

if (!empty_nodes) summary(df.cluster07)

   fecha_cnt           tmax            tmin           precip           nevada 
 Min.   : 1.000   Min.   :138.0   Min.   : 52.0   Min.   : 0.000   Min.   :0  
 1st Qu.: 6.000   1st Qu.:228.0   1st Qu.:125.0   1st Qu.: 1.000   1st Qu.:0  
 Median : 7.000   Median :259.0   Median :151.0   Median : 6.000   Median :0  
 Mean   : 6.992   Mean   :260.5   Mean   :151.5   Mean   : 9.939   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:291.0   3rd Qu.:178.0   3rd Qu.:15.000   3rd Qu.:0  
 Max.   :12.000   Max.   :403.0   Max.   :254.0   Max.   :82.000   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:37.26   1st Qu.: -6.0442   1st Qu.:  32.0  
 Median : 0.00000   Median :39.99   Median : -3.6781   Median :  87.0  
 Mean   : 0.00524   Mean   :38.69   Mean   : -4.1018   Mean   : 292.1  
 3rd Qu.: 0.00000   3rd Qu.:41.66   3rd Qu.:  0.3664   3rd Qu.: 541.0  
 Max.   :38.00000   Max.   :43.57   Max.   :  4.2156   Max.   :2451.0  

if (!empty_nodes) summary(df.cluster08)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : -8.0   Min.   :-58.00   Min.   :102.0   Min.   :0  
 1st Qu.: 5.000   1st Qu.:118.0   1st Qu.: 57.00   1st Qu.:117.0   1st Qu.:0  
 Median :11.000   Median :138.0   Median : 75.00   Median :132.0   Median :0  
 Mean   : 8.764   Mean   :137.8   Mean   : 74.03   Mean   :142.2   Mean   :0  
 3rd Qu.:12.000   3rd Qu.:158.2   3rd Qu.: 95.00   3rd Qu.:150.0   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   :223.00   Max.   :422.0   Max.   :0  
   prof_nieve         longitud        latitud           altitud       
 Min.   : 0.0000   Min.   :27.82   Min.   :-17.889   Min.   :   1.00  
 1st Qu.: 0.0000   1st Qu.:41.77   1st Qu.: -8.616   1st Qu.:  42.98  
 Median : 0.0000   Median :42.43   Median : -8.411   Median : 261.00  
 Mean   : 0.6782   Mean   :41.37   Mean   : -6.061   Mean   : 405.46  
 3rd Qu.: 0.0000   3rd Qu.:42.89   3rd Qu.: -3.107   3rd Qu.: 370.00  
 Max.   :81.0000   Max.   :43.57   Max.   :  3.035   Max.   :2400.00  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.hist(df.cluster07)

if (!empty_nodes) mpr.hist(df.cluster08)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster07)

if (!empty_nodes) mpr.boxplot(df.cluster08)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
  df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
  df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)

Visualización de 10 clústeres:

if (!empty_nodes) {
  plot(hac, hang=-1, labels=F)
  rect.hclust(hac, k=10)
}

Visualización de los clústers en el mapa

A qué clúster pertenece cada nodo del mapa de kohonen:

if (!empty_nodes) {
  groups <- cutree(hac, k=10)
  plot(model, type="mapping",
    bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
    shape = "straight", labels = "")
  add.cluster.boundaries(model, clustering=groups)
}

Análisis de las observaciones de cada cluster

if (!empty_nodes) {
  # Asignamos a cada registro su clúster
  df$cluster <- groups[model$unit.classif]
}

Nuevos dataframes por cluster

if (!empty_nodes) {
  # Creo nuevos dataframes, uno por cada clúster.
  df.cluster01 <- subset(df, cluster==1)
  df.cluster02 <- subset(df, cluster==2)
  df.cluster03 <- subset(df, cluster==3)
  df.cluster04 <- subset(df, cluster==4)
  df.cluster05 <- subset(df, cluster==5)
  df.cluster06 <- subset(df, cluster==6)
  df.cluster07 <- subset(df, cluster==7)
  df.cluster08 <- subset(df, cluster==8)
  df.cluster09 <- subset(df, cluster==9)
  df.cluster10 <- subset(df, cluster==10)

  # Extraigo del dataframe las features.
  df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster09 <- select(df.cluster09, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
  df.cluster10 <- select(df.cluster10, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}

if (!empty_nodes) summary(df.cluster01)

   fecha_cnt           tmax            tmin             precip      
 Min.   : 1.000   Min.   :-47.0   Min.   :-115.00   Min.   : 30.00  
 1st Qu.: 3.000   1st Qu.:126.0   1st Qu.:  55.00   1st Qu.: 48.00  
 Median :10.000   Median :150.0   Median :  75.00   Median : 59.00  
 Mean   : 7.941   Mean   :145.4   Mean   :  71.63   Mean   : 62.29  
 3rd Qu.:11.000   3rd Qu.:176.0   3rd Qu.:  99.00   3rd Qu.: 74.00  
 Max.   :12.000   Max.   :326.0   Max.   : 211.00   Max.   :155.00  
     nevada    prof_nieve         longitud        latitud       
 Min.   :0   Min.   : 0.0000   Min.   :27.82   Min.   :-17.889  
 1st Qu.:0   1st Qu.: 0.0000   1st Qu.:40.78   1st Qu.: -6.829  
 Median :0   Median : 0.0000   Median :42.44   Median : -3.831  
 Mean   :0   Mean   : 0.1989   Mean   :41.35   Mean   : -3.890  
 3rd Qu.:0   3rd Qu.: 0.0000   3rd Qu.:43.31   3rd Qu.: -0.482  
 Max.   :0   Max.   :58.0000   Max.   :43.57   Max.   :  4.216  
    altitud      
 Min.   :   1.0  
 1st Qu.:  42.0  
 Median : 143.0  
 Mean   : 443.8  
 3rd Qu.: 445.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster02)

   fecha_cnt       tmax            tmin             precip          nevada 
 Min.   : 6   Min.   :-24.0   Min.   :-110.00   Min.   : 0.00   Min.   :0  
 1st Qu.:10   1st Qu.:116.0   1st Qu.:  28.00   1st Qu.: 5.00   1st Qu.:0  
 Median :11   Median :155.0   Median :  60.00   Median :12.00   Median :0  
 Mean   :11   Mean   :150.6   Mean   :  57.68   Mean   :14.31   Mean   :0  
 3rd Qu.:12   3rd Qu.:187.0   3rd Qu.:  89.00   3rd Qu.:22.00   3rd Qu.:0  
 Max.   :12   Max.   :248.0   Max.   : 150.00   Max.   :59.00   Max.   :0  
   prof_nieve        longitud        latitud           altitud      
 Min.   : 0.000   Min.   :27.92   Min.   :-17.755   Min.   :   1.0  
 1st Qu.: 0.000   1st Qu.:39.48   1st Qu.: -4.699   1st Qu.:  85.0  
 Median : 0.000   Median :40.96   Median : -2.357   Median : 521.0  
 Mean   : 0.088   Mean   :40.35   Mean   : -2.570   Mean   : 589.6  
 3rd Qu.: 0.000   3rd Qu.:42.08   3rd Qu.:  0.595   3rd Qu.: 790.0  
 Max.   :59.000   Max.   :43.57   Max.   :  4.216   Max.   :2535.0  

if (!empty_nodes) summary(df.cluster03)

   fecha_cnt           tmax            tmin             precip      
 Min.   : 1.000   Min.   :-53.0   Min.   :-121.00   Min.   :  0.00  
 1st Qu.: 1.000   1st Qu.:119.0   1st Qu.:  21.00   1st Qu.:  5.00  
 Median : 3.000   Median :151.0   Median :  48.00   Median : 12.00  
 Mean   : 2.623   Mean   :145.4   Mean   :  44.88   Mean   : 15.62  
 3rd Qu.: 4.000   3rd Qu.:174.0   3rd Qu.:  72.00   3rd Qu.: 24.00  
 Max.   :12.000   Max.   :259.0   Max.   : 157.00   Max.   :126.00  
     nevada    prof_nieve          longitud        latitud        
 Min.   :0   Min.   :  0.0000   Min.   :27.82   Min.   :-17.8889  
 1st Qu.:0   1st Qu.:  0.0000   1st Qu.:39.48   1st Qu.: -4.8500  
 Median :0   Median :  0.0000   Median :41.11   Median : -2.6544  
 Mean   :0   Mean   :  0.4464   Mean   :40.43   Mean   : -2.7337  
 3rd Qu.:0   3rd Qu.:  0.0000   3rd Qu.:42.18   3rd Qu.:  0.4942  
 Max.   :0   Max.   :197.0000   Max.   :43.57   Max.   :  4.2156  
    altitud      
 Min.   :   1.0  
 1st Qu.:  71.0  
 Median : 442.0  
 Mean   : 511.9  
 3rd Qu.: 775.0  
 Max.   :2535.0  

if (!empty_nodes) summary(df.cluster04)

   fecha_cnt          tmax             tmin            precip           nevada 
 Min.   :1.000   Min.   :-12.00   Min.   :-72.00   Min.   : 19.00   Min.   :0  
 1st Qu.:2.000   1st Qu.:  9.50   1st Qu.:-43.50   1st Qu.: 41.00   1st Qu.:0  
 Median :2.000   Median : 15.00   Median :-34.00   Median : 56.00   Median :0  
 Mean   :2.368   Mean   : 23.21   Mean   :-31.95   Mean   : 65.79   Mean   :0  
 3rd Qu.:3.000   3rd Qu.: 28.50   3rd Qu.:-25.00   3rd Qu.: 82.00   3rd Qu.:0  
 Max.   :4.000   Max.   : 76.00   Max.   :  8.00   Max.   :180.00   Max.   :0  
   prof_nieve       longitud        latitud         altitud    
 Min.   :465.0   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:531.0   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :606.0   Median :40.78   Median :-4.01   Median :1894  
 Mean   :612.8   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:661.5   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :892.0   Max.   :40.78   Max.   :-4.01   Max.   :1894  

if (!empty_nodes) summary(df.cluster05)

   fecha_cnt      tmax            tmin        precip          nevada   
 Min.   :2   Min.   :71.00   Min.   :-1   Min.   :14.00   Min.   :4.0  
 1st Qu.:2   1st Qu.:72.25   1st Qu.:-1   1st Qu.:15.25   1st Qu.:4.5  
 Median :2   Median :73.50   Median :-1   Median :16.50   Median :5.0  
 Mean   :2   Mean   :73.50   Mean   :-1   Mean   :16.50   Mean   :5.0  
 3rd Qu.:2   3rd Qu.:74.75   3rd Qu.:-1   3rd Qu.:17.75   3rd Qu.:5.5  
 Max.   :2   Max.   :76.00   Max.   :-1   Max.   :19.00   Max.   :6.0  
   prof_nieve    longitud        latitud          altitud     
 Min.   : 5   Min.   :40.48   Min.   :-3.450   Min.   :263.0  
 1st Qu.: 7   1st Qu.:40.78   1st Qu.:-2.846   1st Qu.:349.3  
 Median : 9   Median :41.08   Median :-2.242   Median :435.6  
 Mean   : 9   Mean   :41.08   Mean   :-2.242   Mean   :435.6  
 3rd Qu.:11   3rd Qu.:41.37   3rd Qu.:-1.637   3rd Qu.:521.8  
 Max.   :13   Max.   :41.67   Max.   :-1.033   Max.   :608.1  

if (!empty_nodes) summary(df.cluster06)

   fecha_cnt         tmax             tmin            precip     
 Min.   : 1.0   Min.   : 69.00   Min.   :-24.00   Min.   : 3.00  
 1st Qu.: 2.0   1st Qu.: 86.25   1st Qu.:-12.50   1st Qu.: 6.00  
 Median : 7.0   Median : 92.00   Median :  6.50   Median :10.50  
 Mean   : 6.8   Mean   : 96.80   Mean   :  7.90   Mean   :11.40  
 3rd Qu.:12.0   3rd Qu.: 96.75   3rd Qu.: 29.25   3rd Qu.:14.75  
 Max.   :12.0   Max.   :140.00   Max.   : 35.00   Max.   :24.00  
     nevada       prof_nieve     longitud        latitud          altitud     
 Min.   :1.00   Min.   :0.0   Min.   :40.48   Min.   :-3.450   Min.   :263.0  
 1st Qu.:1.25   1st Qu.:0.0   1st Qu.:40.48   1st Qu.:-3.450   1st Qu.:263.0  
 Median :2.00   Median :0.5   Median :41.08   Median :-2.242   Median :435.6  
 Mean   :1.80   Mean   :1.1   Mean   :41.08   Mean   :-2.242   Mean   :435.6  
 3rd Qu.:2.00   3rd Qu.:1.0   3rd Qu.:41.67   3rd Qu.:-1.033   3rd Qu.:608.1  
 Max.   :3.00   Max.   :7.0   Max.   :41.67   Max.   :-1.033   Max.   :608.1  

if (!empty_nodes) summary(df.cluster07)

   fecha_cnt           tmax             tmin            precip          nevada 
 Min.   : 1.000   Min.   :-12.00   Min.   :-63.00   Min.   : 5.00   Min.   :0  
 1st Qu.: 1.250   1st Qu.: 13.50   1st Qu.:-38.00   1st Qu.:25.25   1st Qu.:0  
 Median : 2.000   Median : 27.50   Median :-26.00   Median :47.00   Median :0  
 Mean   : 3.342   Mean   : 31.89   Mean   :-25.89   Mean   :46.53   Mean   :0  
 3rd Qu.: 3.750   3rd Qu.: 47.75   3rd Qu.:-15.00   3rd Qu.:66.75   3rd Qu.:0  
 Max.   :12.000   Max.   : 96.00   Max.   : 28.00   Max.   :92.00   Max.   :0  
   prof_nieve       longitud        latitud         altitud    
 Min.   :199.0   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:229.5   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :270.0   Median :40.78   Median :-4.01   Median :1894  
 Mean   :280.6   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:318.0   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :429.0   Max.   :40.78   Max.   :-4.01   Max.   :1894  

if (!empty_nodes) summary(df.cluster08)

   fecha_cnt        tmax           tmin           precip         nevada 
 Min.   :2.0   Min.   :-4.0   Min.   :-51.0   Min.   :19.0   Min.   :0  
 1st Qu.:2.0   1st Qu.: 0.0   1st Qu.:-48.0   1st Qu.:33.0   1st Qu.:0  
 Median :3.0   Median :35.0   Median :-26.0   Median :49.0   Median :0  
 Mean   :2.6   Mean   :20.6   Mean   :-32.8   Mean   :41.4   Mean   :0  
 3rd Qu.:3.0   3rd Qu.:36.0   3rd Qu.:-23.0   3rd Qu.:50.0   3rd Qu.:0  
 Max.   :3.0   Max.   :36.0   Max.   :-16.0   Max.   :56.0   Max.   :0  
   prof_nieve      longitud        latitud         altitud    
 Min.   :1017   Min.   :40.78   Min.   :-4.01   Min.   :1894  
 1st Qu.:1073   1st Qu.:40.78   1st Qu.:-4.01   1st Qu.:1894  
 Median :1168   Median :40.78   Median :-4.01   Median :1894  
 Mean   :1317   Mean   :40.78   Mean   :-4.01   Mean   :1894  
 3rd Qu.:1494   3rd Qu.:40.78   3rd Qu.:-4.01   3rd Qu.:1894  
 Max.   :1834   Max.   :40.78   Max.   :-4.01   Max.   :1894  

if (!empty_nodes) summary(df.cluster09)

   fecha_cnt           tmax            tmin           precip           nevada 
 Min.   : 1.000   Min.   :138.0   Min.   : 52.0   Min.   : 0.000   Min.   :0  
 1st Qu.: 6.000   1st Qu.:228.0   1st Qu.:125.0   1st Qu.: 1.000   1st Qu.:0  
 Median : 7.000   Median :259.0   Median :151.0   Median : 6.000   Median :0  
 Mean   : 6.992   Mean   :260.5   Mean   :151.5   Mean   : 9.939   Mean   :0  
 3rd Qu.: 9.000   3rd Qu.:291.0   3rd Qu.:178.0   3rd Qu.:15.000   3rd Qu.:0  
 Max.   :12.000   Max.   :403.0   Max.   :254.0   Max.   :82.000   Max.   :0  
   prof_nieve          longitud        latitud            altitud      
 Min.   : 0.00000   Min.   :27.82   Min.   :-17.8889   Min.   :   1.0  
 1st Qu.: 0.00000   1st Qu.:37.26   1st Qu.: -6.0442   1st Qu.:  32.0  
 Median : 0.00000   Median :39.99   Median : -3.6781   Median :  87.0  
 Mean   : 0.00524   Mean   :38.69   Mean   : -4.1018   Mean   : 292.1  
 3rd Qu.: 0.00000   3rd Qu.:41.66   3rd Qu.:  0.3664   3rd Qu.: 541.0  
 Max.   :38.00000   Max.   :43.57   Max.   :  4.2156   Max.   :2451.0  

if (!empty_nodes) summary(df.cluster10)

   fecha_cnt           tmax            tmin            precip          nevada 
 Min.   : 1.000   Min.   : -8.0   Min.   :-58.00   Min.   :102.0   Min.   :0  
 1st Qu.: 5.000   1st Qu.:118.0   1st Qu.: 57.00   1st Qu.:117.0   1st Qu.:0  
 Median :11.000   Median :138.0   Median : 75.00   Median :132.0   Median :0  
 Mean   : 8.764   Mean   :137.8   Mean   : 74.03   Mean   :142.2   Mean   :0  
 3rd Qu.:12.000   3rd Qu.:158.2   3rd Qu.: 95.00   3rd Qu.:150.0   3rd Qu.:0  
 Max.   :12.000   Max.   :350.0   Max.   :223.00   Max.   :422.0   Max.   :0  
   prof_nieve         longitud        latitud           altitud       
 Min.   : 0.0000   Min.   :27.82   Min.   :-17.889   Min.   :   1.00  
 1st Qu.: 0.0000   1st Qu.:41.77   1st Qu.: -8.616   1st Qu.:  42.98  
 Median : 0.0000   Median :42.43   Median : -8.411   Median : 261.00  
 Mean   : 0.6782   Mean   :41.37   Mean   : -6.061   Mean   : 405.46  
 3rd Qu.: 0.0000   3rd Qu.:42.89   3rd Qu.: -3.107   3rd Qu.: 370.00  
 Max.   :81.0000   Max.   :43.57   Max.   :  3.035   Max.   :2400.00  

Número de elementos en cada clúster

if (!empty_nodes) {
  df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1], dim(df.cluster09)[1], dim(df.cluster10)[1])
  barplot(df.clusters.dim,
          names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08", "cluster09", "cluster10"),
          col = "steelblue1")
}

Distribución de los datos

if (!empty_nodes) mpr.hist(df.cluster01)

if (!empty_nodes) mpr.hist(df.cluster02)

if (!empty_nodes) mpr.hist(df.cluster03)

if (!empty_nodes) mpr.hist(df.cluster04)

if (!empty_nodes) mpr.hist(df.cluster05)

if (!empty_nodes) mpr.hist(df.cluster06)

if (!empty_nodes) mpr.hist(df.cluster07)

if (!empty_nodes) mpr.hist(df.cluster08)

if (!empty_nodes) mpr.hist(df.cluster09)

if (!empty_nodes) mpr.hist(df.cluster10)

if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)

if (!empty_nodes) mpr.boxplot(df.cluster03)

if (!empty_nodes) mpr.boxplot(df.cluster04)

if (!empty_nodes) mpr.boxplot(df.cluster05)

if (!empty_nodes) mpr.boxplot(df.cluster06)

if (!empty_nodes) mpr.boxplot(df.cluster07)

if (!empty_nodes) mpr.boxplot(df.cluster08)

if (!empty_nodes) mpr.boxplot(df.cluster09)

if (!empty_nodes) mpr.boxplot(df.cluster10)

Localización geográfica de las estaciones de medida de cada cluster

# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
  df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
  df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
  df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
  df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
  df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
  df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
  df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
  df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
  df.cluster09.grouped <- mpr.group_by_geo(df.cluster09)
  df.cluster10.grouped <- mpr.group_by_geo(df.cluster10)
}

if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster09.grouped)

if (!empty_nodes) mpr.draw_map(spain, df.cluster10.grouped)